Hi guys,
I've recently worked on a project supervised by a lecturer in the school of psychology at my university. She's been looking over the article I've written and is concerned that my use of non-parametric statistical techniques through much of the data analysis is a problem, saying that publishers don't like to publish articles with non-parametric statistics - and that their use is perceived in psychology as indicating lacking methodology. This is the first time I've come across this notion, either within psychology or the broader social sciences.
I'm not particularly concerned about whether I should've used non-para techniques in the case at hand - it's a pretty open and shut case, a number of variables not being within coo-ee of normal distribution and so on. I was wondering, though, whether anyone else has come across this curious perception? I've always seen non-parametric methods as MORE methodologically vigorous, given that they require (by definition) fewer or no assumptions about the data at hand - assumptions that often aren't valid in real life data.
So - is this a peculiar misunderstanding on my supervisor's part, a commonly-held but inaccurate perception about non-parametric models, or can such models really be a tip-off of methodological weaknesses?
I've recently worked on a project supervised by a lecturer in the school of psychology at my university. She's been looking over the article I've written and is concerned that my use of non-parametric statistical techniques through much of the data analysis is a problem, saying that publishers don't like to publish articles with non-parametric statistics - and that their use is perceived in psychology as indicating lacking methodology. This is the first time I've come across this notion, either within psychology or the broader social sciences.
I'm not particularly concerned about whether I should've used non-para techniques in the case at hand - it's a pretty open and shut case, a number of variables not being within coo-ee of normal distribution and so on. I was wondering, though, whether anyone else has come across this curious perception? I've always seen non-parametric methods as MORE methodologically vigorous, given that they require (by definition) fewer or no assumptions about the data at hand - assumptions that often aren't valid in real life data.
So - is this a peculiar misunderstanding on my supervisor's part, a commonly-held but inaccurate perception about non-parametric models, or can such models really be a tip-off of methodological weaknesses?
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